Adaptive Zone–Assisted Iterative Localization in Energy-Efficient Wireless Sensor Networks

Wei, Chun-Yi; Pan, Hsuan-Yi

doi:10.1109/jsen.2021.3120883

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…There are two main types of localization problems in WSN: one is the self-localization of sensor nodes, and the other is the use of WSN to obtain the location information of the target. The target can be a sensor node, a device, or an event [4]. Since localization system modules such as GPS are too expensive to be installed on every node in the network, only a small fraction of the nodes has a known location, and these nodes are called anchor nodes.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

Lei Wang,

Ting-Ting Niu

et al. 2024

Journal of Computers

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<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

Lei Wang,

Ting-Ting Niu

et al. 2024

Journal of Computers

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Energy-efficient power scheduling and allocation scheme for wireless sensor networks

Chen

2022

Energy Reports

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Cube-Based Multitarget 3D Localization Using Bayesian Learning-Based Turbo Decoding in Wireless Sensor Networks

Wei

Chen

2022

IEEE Sensors J.

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Adaptive Zone–Assisted Iterative Localization in Energy-Efficient Wireless Sensor Networks

Cited by 4 publications

References 25 publications

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

Energy-efficient power scheduling and allocation scheme for wireless sensor networks

Cube-Based Multitarget 3D Localization Using Bayesian Learning-Based Turbo Decoding in Wireless Sensor Networks

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